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HEMOSTASIS, THROMBOSIS, AND VASCULAR BIOLOGY
Genetic associations with thalidomide mediated venous thrombotic events in
myeloma identified using targeted genotyping
David C. Johnson,1 Sophie Corthals,2 Christine Ramos,3 Antje Hoering,4 Kim Cocks,5 Nicholas J. Dickens,1 Jeff Haessler,4
Harmut Goldschmidt,6 J. Anthony Child,5 Sue E. Bell,5 Graham Jackson,7 Dalsu Baris,8 S. Vincent Rajkumar,9
Faith E. Davies,1 Brian G. M. Durie,10 John Crowley,4 Pieter Sonneveld,2 Brian Van Ness,3 and Gareth J. Morgan1
1Section of Haemato-Oncology, Institute of Cancer Research, London, United Kingdom; 2Department of Hematology, Erasmus Medical Center, Rotterdam, The
Netherlands; 3Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis; 4Cancer Research and Biostatistics (CRAB),
Seattle, WA; 5Clinical Trials Research Unit, University of Leeds, Leeds, United Kingdom; 6Department of Internal Medicine, Division of Hematology/Oncology,
Amyloidosis Clinic, University of Heidelberg, Heidelberg, Germany; 7School of Laboratory and Clinical Sciences, University of Newcastle, Newcastle, United
Kingdom; 8Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD; 9Department of Internal Medicine, Division of Hematology,
Mayo Clinic, Rochester, MN; and 10International Myeloma Foundation and Cedars Sinai Comprehensive Cancer Center, Los Angeles, CA
A venous thromboembolism (VTE) with
the subsequent risk of pulmonary embolism is a major concern in the treatment of
patients with multiple myeloma with thalidomide. The susceptibility to developing a VTE in response to thalidomide
therapy is likely to be influenced by both
genetic and environmental factors. To
test genetic variation associated with
treatment related VTE in patient peripheral blood DNA, we used a custom-built
molecular inversion probe (MIP)–based
single nucleotide polymorphism (SNP)
chip containing 3404 SNPs. SNPs on the
chip were selected in “functional regions” within 964 genes spanning 67 molecular pathways thought to be involved
in the pathogenesis, treatment response,
and side effects associated with myeloma
therapy. Patients and controls were taken
from 3 large clinical trials: Medical Research Council (MRC) Myeloma IX, Hovon50, and Eastern Cooperative Oncology
Group (ECOG) EA100, which compared
conventional treatments with thalidomide
in patients with myeloma. Our analysis
showed that the set of SNPs associated
with thalidomide-related VTE were en-
riched in genes and pathways important
in drug transport/metabolism, DNA repair, and cytokine balance. The effects of
the SNPs associated with thalidomiderelated VTE may be functional at the level
of the tumor cell, the tumor-related microenvironment, and the endothelium. The
clinical trials described in this paper have
been registered as follows: MRC Myeloma IX: ISRCTN68454111; Hovon-50:
NCT00028886; and ECOG EA100:
NCT00033332.
(Blood.
2008;112:
4924-4934)
Introduction
The introduction of thalidomide and other immunomodulatory
drugs has revolutionized clinical management of patients with
myeloma. Thalidomide treatment has achieved response rates of
30% at relapse and even higher rates at presentation.1 Investigation
of the specific effects of thalidomide in myeloma remains an active
area of research where up-regulation of ICAM-1,2 VCAM-1,
IL-10, and 3,4 IL-12,5 and decreased levels of VEGF,6 ␤FGF,7-9
HGF,10 TNF␣,11 IL-6,12 and soluble IL-6 receptor (sIL-6R)13 are
thought to play a role in the mechanism of action, which suggests
that thalidomide effects the myeloma cell directly as well as its
microenvironment.14
The therapeutic use of thalidomide has focused attention on
venous thrombotic events (VTEs). There appears to be a background rate of 5% to 10% VTE15,16 in myeloma possibly due to
enhanced expression of tissue factor and VEGF,17 acquired cytokinemediated activated protein C resistance,18 and down-regulation of
thrombospondin.19 In intensively treated patients exposed to thalidomide, the rate of VTE increases to 10% to 15%16,20,21; the
mechanisms leading to this are uncertain, but it is known that
thalidomide regulates the level of COX-2,22-25 a well described
prothrombotic factor. Thalidomide may also modulate the VTE risk
by its effects on cytokine levels acting on the endothelial cell, a
mechanism dependent on the differential apoptotic effects of
thalidomide in myeloma plasma cells compared with endothelial
cells, which are protected from apoptosis by decrease of VEGF by
thalidomide.26-28 In this context, it is known that stressed human
umbilical vein endothelial cells (HUVECs) up-regulate a number
of procoagulant factors, including PAR-1, P-selectin, E-selectin,
and tissue factor. Thalidomide protects these cells from apoptosis
potentially enhancing these procoagulant effects, and there is some
clinical evidence for this mechanism in non-myeloma settings.29-33
The risk of developing a VTE following thalidomide exposure
depends upon a number of factors, including disease stage, the type
of chemotherapy combination, and the supportive therapy used.
Patient-specific variables also contribute to the excess risk of VTE,
including immobility, poor performance status, and dehydration.
An important clinical observation is that VTEs occur early after the
initiation of thalidomide treatment. VTE rates are also increased in
patients when used in conjunction with anthracycline and dexamethasone,34,35 and can decrease following exposure to bortezomib.36-40
The excess risk of thalidomide associated VTE in myeloma has
been managed by a number of different strategies, ranging from the
Submitted February 22, 2008; accepted August 24, 2008. Prepublished online
as Blood First Edition paper, September 19, 2008; DOI 10.1182/blood-200802-140434.
The publication costs of this article were defrayed in part by page charge
payment. Therefore, and solely to indicate this fact, this article is hereby
marked ‘‘advertisement’’ in accordance with 18 USC section 1734.
The online version of this article contains a data supplement.
4924
BLOOD, 15 DECEMBER 2008 䡠 VOLUME 112, NUMBER 13
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BLOOD, 15 DECEMBER 2008 䡠 VOLUME 112, NUMBER 13
identification of high-risk patients suitable for prophylaxis to
prophylactic anticoagulation for all patients.41 Aspirin has been
suggested to be effective,42 but its use is controversial because of
the lack of a readily applicable mechanism justifying its use. In this
work, we have examined inherited genetic variation associated
with VTE following thalidomide exposure in patients with myeloma using a custom array-based single nucleotide polymorphism
(SNP) detection tool in an effort to elucidate the molecular
mechanisms contributing to increased risk.
Methods
Clinical samples
Peripheral blood DNA samples were obtained from 544 patients with
myeloma derived from 3 randomized clinical trials comparing standard
induction treatment for presenting patients with thalidomide containing
regimens derived from the Medical Research Council (MRC) Myeloma IX
(1966 patients), the Eastern Cooperative Oncology Group (ECOG) EA100
(900 patients), and the Hovon-50 study (400 patients; Figure 1). The dose of
thalidomide (100-200 mg daily) was comparable between the 3 studies, but
the chemotherapy combinations used differed. The samples were used as
the basis for 2 nested case-control comparisons examining the inherited
genetic contribution to the risk of VTE as a consequence of thalidomide
exposure. In a discovery set analysis, we compared the genotype results
derived from 157 Myeloma IX patients with VTEs, of which 104 were
related to thalidomide exposure and 53 were unrelated, to a control group of
315 age- and sex-matched patients with myeloma also in the trial, who did
not develop a VTE (198 thalidomide-exposed patients and 117 non–
thalidomide-exposed patients). To validate the frequency distributions, we
carried out a second case-control comparison using 23 patients with VTE
treated with thalidomide and 49 thalidomide-treated controls. To ensure
homogeneity of allelic frequencies, only patients of European descent were
included. This study has been approved by The United Kingdom Multicentre Ethics Committee.
Clinical trials
The Myeloma IX study comprises 2 randomizations: an intensive pathway
for younger, fitter patients comparing CVAD (cyclophosphamide 500 mg
orally weekly, vincristine 0.4 mg intravenously on days [d] 1-4), doxorubicin 9.0 mg/m2 on d1-d4, dexamethasone 40 mg on d1-d4 and d12-d15),
delivered by a central venous access device with oral CTD (cyclophosphamide, thalidomide, dexamethasone) using the same doses of cyclophosphamide and dexamethasone combined with 200 mg of thalidomide. The
second randomization, for older, less-fit patients, compared an attenuated
dose of CTD (thalidomide 100-200 mg) to melphalan (7.0 mg/m2 orally on
d1-d4 every 28 days) and prednisolone (MP). All patients at high risk of
VTE, defined by clinical criteria, were identified; prophylactic anticoagulation was considered by the treating physician, but it was not specified. The
ECOG EA100 study randomized patients to either dexamethasone alone
40 mg daily from d1 to d4 and d12 to d15 or the same dose in combination
with thalidomide 200 mg daily. In the study set, from which samples
were available, no thromboprophylaxis was used on either arm. The
Hovon-50 study randomized patients to either 3 cycles of VAD
(vincristine 0.4 mg, intravenous rapid infusion on d1-d4; doxorubicin
9 mg/m2, intravenous rapid infusion on d1-d4; and dexamethasone
40 mg orally, d1-d4, d9-d12, and d17-d20) or the same regimen but with
thalidomide replacing the vincristine (TAD). Thalidomide was given
daily at a dose of 200 mg, but could be escalated to 400 mg. All patients
in the TAD arm received thromboprophylaxis with low-molecularweight heparin (LMWH). Incident cases of VTE were defined using
clinical criteria, and no screening approach was used. The identification
of VTE represents current clinical practice with initial clinical identification and subsequent confirmation and definition of the extent of
thrombosis using a definitive radiologic investigation. Central venous
SNPS ASSOCIATED WITH THALIDOMIDE VTES IN MYELOMA
4925
thrombosis and line-related thrombosis were defined by clinical criteria
and subsequently confirmed by ultrasound.
Genotyping, SNP selection, and chip design
DNA was extracted from frozen white blood cell pellets using the QIAGEN
Flexigene kit (Valencia, CA) and quantified using a Nanodrop spectrophotometer (Wilmington, DE). Genotyping was performed using the Affymetrix targeted genotyping platform (Santa Clara, CA), which is based on
a molecular inversion probe technology.43-45 Patient samples were assayed
using a custom-built 3.0K panel comprising 3400 SNPs. SNPs were
selected using a hypothesis-driven strategy. Pertinent candidate genes were
nominated by myeloma groups in the International Myeloma Foundation–
led “Bank On A Cure” (BOAC) consortium. An initial list was supplemented with referencing pathway databases, including BioCarta, Kyoto
Encyclopedia of Genes and Genomes (KEGG),46,47 and Pathway Assist
(Ariadne Genomics, Rockville, MD),48 generating a candidate gene list
spanning some 67 molecular pathways important in the biology of
myeloma, treatment response, and side effects to conventional and novel
agents, which included important genes within the clotting and prothrombotic pathways. Taking the BOAC candidate genes, we completed a
literature search49 to identify SNPs that had been previously reported as
having a functional consequence or relevance in prior etiologic or
treatment outcome studies. SNPs with a minor allele frequency (MAF)
greater than 2% were then systematically selected from the candidate
gene list using the following criteria: nonsynonymous SNPs present in
dbSNP/SNP 50050; promoter variants present in homologous regions
between human and mouse, in or adjacent to a transcription binding site
using the Promolign database51; and promoter SNPs identified in the
Functional Element SNPs Database (FESD).52 We then included TagSNPs in genes considered to be of particular relevance along with
population discriminating admixture variants from the X chromosome.53
Finally, we included all nonsynonymous SNPs present in the dbSNP
database in phosphatase, kinase, and transferase genes with a MAF
greater than 2%. The genes and SNPs comprising this panel with allele
frequencies are available online (see Johnson54).
Statistical analyses
We carried out a Fisher exact Hardy-Weinberg equilibrium (HWE) test at a
P value less than or equal to .001 on all SNPs across the control samples and
removed SNPs departing from HWE from the analysis to filter erroneously
performing SNPs. We then carried out a “test of missing-ness” on patient
and control status to control for any bias in missing data. We performed a
basic Fisher (allelic) association test for disease trait based on a comparison
of patients with controls. We then completed the analysis using 3 genetic
models: additive (Cochran-Armitage trend test), dominant, and recessive.
To account for multiple testing, we carried out label-swapping permutation
procedures on each of the SNP assays, with their most significant models
used to calculate an empirical P value for each SNP. The size of the dataset
generated on the BOAC panel is much larger than a typical candidate-gene
study; we therefore carried out this analysis in the program PLINK,55 an
open-source whole genome association analysis toolset designed for large
dataset analysis. The test for epistasis involved testing all pairwise
combinations of SNPs. The output consists only of pairwise epistatic results
above a P level less than .001 for each SNP. Combinations were restricted to
SNPs more than 1 MB apart or on different chromosomes. This test is only
an approximation of the extent of epistasis (SNP-SNP interaction), as it is a
naive statistic that does not take linkage disequilibrium (LD) into account.
We characterized the haplotypes using Haploview 4.0,56 and completed
haplotype trend regression in Helix-tree (Bozeman, MT). Meta-analysis
was performed in SPSS 14.0 (Chicago, IL) using a meta-analysis macro
written by Garcia-Granero.57 Combined odds ratios were calculated using
the Mantel Haenszel method for fixed events.
Biological relevance of the associated SNPs
To examine the possible functionality of the thalidomide-related VTEassociated SNPs, we used 2 complementary in silico algorithms for
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4926
BLOOD, 15 DECEMBER 2008 䡠 VOLUME 112, NUMBER 13
JOHNSON et al
Figure 1. Simplified treatment arms of ECOG EA100,
Hovon-50, and Myeloma IX studies.
prediction of the putative impact of missense variants on protein function,
PolyPhen58 (structural) and the SIFT59 (conservation), shown in Table S8
(available on the Blood website; see the Supplemental Materials link at the
top of the online article). We then used a bioinformatics approach to define
the pathways potentially deregulated by the associated and validated genes.
We used the functional annotation tool on the DAVID Bioinformatics
Resources/Database60 to characterize which pathways are most represented
in associated gene groups from our single-point analysis. The gene
coverage of the BOAC chip was used to form a template/background set,
against which associated genes and validated associated SNPs with VTE
recursive partitioning consists of cross-validation by trimming back (pruning) the typically complex full tree. The best pruned trees are examined to
find which one has the largest classification rate while using the smallest
number of SNPs. Sensitivity and specificity are determined for the training
and validation set. A receiver operator characteristic (ROC) curve is used to
determine the best sensitivity and specificity trade-off.
were tested (Table S9).
Clinical results
Recursive partitioning
The Myeloma IX analysis is based on 1966 randomized patients:
984 patients treated with CTD, 557 patients treated with CVAD,
and 425 patients treated with MP. In the intensive pathway, the
overall rate of VTE was identical in both arms (Table S1).
However, there was a qualitative difference between the 2 arms,
with deep vein thrombosis (DVT) predominant in the thalidomidetreated group and line-related thrombosis predominant in the CVAD
group. In the nonintensive pathway, very few VTEs were seen in the MP
To develop a predictive model for the identification of patients at high risk
of VTE, we first divided the combined dataset into a training and validation
set. We then applied the method of recursive partitioning to the training
set.61 In this approach, a regression tree is built by first finding the SNP
which best splits the data into 2 groups (VTE, no VTE). This process is
repeated over and over again for the individual subsets until the subgroups
reach a minimum size or no improvement can be made. The second stage in
Results
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BLOOD, 15 DECEMBER 2008 䡠 VOLUME 112, NUMBER 13
SNPS ASSOCIATED WITH THALIDOMIDE VTES IN MYELOMA
4927
Figure 2. Venn diagram showing overlapping VTE-associated genes between thalidomide Myeloma IX, non-thalidomide Myeloma IX, and thalidomide
Hovon-50/ECOG EA100 analyses.
group, whereas in the CTD group there was a 15.0% VTE rate. The
median time to VTE in each of the groups was approximately 12 weeks
from treatment initiation. The Hovon-50 study had VTE rates of 12.1%
and 11.8% in the thalidomide-related and standard arms, respectively,
with median time to first event of 8.9 weeks. In the ECOG EA100 study,
the VTE rates were 17.0% and 3.0% in the thalidomide-related and
standard arms, respectively.62
Panel, sample, and SNP assay validation
Affymetrix constructed and validated the SNP panel reagents.
A total of 59 DNA samples from the extensively characterized and
genotyped Coriell CEPH HAPMAP series were assayed to validate
the call performance of the BOAC panel. A total of 58 Coriell
CEPH HAPMAP samples were also used in a correlation analysis
between the BOAC chip and HAPMAP study. We did not obtain
Hapmap data for the remaining Coriell samples and did not
perform a correlation analysis. A total of 2606 SNPs were present
on both the chip and HAPMAP. There was a SNP call correlation of
96.1% at 95% confidence levels; SNPs falling below this were
removed from the analysis (132 SNPs). The Coriell sample
genotype validation was replicated in BOAC labs to ensure there
was no differential bias in genotyping scoring between sites.
Patients and controls were also genotyped together throughout the
experiment to avoid any differential bias in genotype scoring. We
observed complete agreement between the known sex and inferred
SNP-based sex in all samples. We used Eigenstrat63 to highlight
population stratification and removed 4 population outliers from the
analysis. A number of admixture SNPs were included in the SNP
panel.53 Genotype calls for these SNPs demonstrated that patients and
controls reflected a sample set drawn from a European population.
Genotyping results and validation
The MRC Myeloma IX study is the largest of the datasets in this
study; to capitalize on this, we chose to focus our discovery set on
this study and to validate the results on combined data sets from
Hovon-50 and ECOG EA100 trials. A set of SNPs for validation
was defined by separately determining the distribution of the most
significant SNPs in the MRC myeloma IX, Hovon-50, and ECOG
EA100 studies. Following testing allelic distributions using the
Fisher test to an empirical P value less than .05 in the discovery set,
120 SNPs were found to be associated with thalidomide-associated
VTE, involving 71 genes (Table S2). Further genetic model
association analysis in Myeloma IX data are listed in Table S3;
allelic and genetic model association for non–thalidomide-related
VTEs are listed in Tables S4 and S5, with allelic and genetic model
association for Hovon-50/EA100 trials shown in Tables S6 and S7. With
the aim of identifying genes modulating the risk of thalidomide-related
thrombosis, we compared the distribution of the SNPs identified in this
analysis between 3 subgroups: thalidomide-associated VTEs from
MRC Myeloma IX, non–thalidomide-associated VTEs from MRC
Myeloma IX, and thalidomide-associated VTEs from the combined
Hovon-50/ECOG EA100 studies.AVenn diagram depicting the overlapping associated genes in the 3 analyses is shown in Figure 2.
Validation of thalidomide-related VTE-associated SNPs
To validate the genotyping results in the discovery set, we
discarded SNPs with conflicting frequency distributions between
studies. This approach may have led to the removal of a number of
true positives from the analysis because of the small sizes of the
Hovon-50 and ECOG EA100 datasets. SNPs with small effect sizes
4
1
6
rs3774968
rs1805414
rs2267669
PPARD
PARP1
NFKB1
NAT2
MT
LIG1
LEP
IL12A
HMMR
Intron
Coding-synon
Intron
Locus
Coding-nonsynon
Untranslated
Promoter
Intron
Coding-nonsynon
Coding-nonsynon
Coding-nonsynon
Coding-nonsynon
Coding-nonsynon
Untranslated
Coding-synon
Functional class
0.1198
0
0.1667
G
G
0.3333
C
G
0.2222
C
0.4
A
0.25
0.3333
A
C
0.355
0.25
T
A
0.2222
T
0.25
C
0.215
0.2222
C
T
0.2576
0
T
C
0.05556
T
0.3333
T
0.065
0.5556
T
0.405
0.5833
A
T
0.5
A
T
0.515
0.3333
T
A
0.3889
T
0.1667
C
0.28
0.1667
C
T
0.1616
0.25
T
C
0.2222
T
0.5
T
0.2323
0.3889
T
0.3081
0.25
T
T
0.1111
T
T
0.06633
0.08333
C
T
0.2778
C
0
A
0.19
0.375
A
C
0.085
MAF
patients
A
Minor
allele
NA indicates not applicable; L95, lower 95% CI; and U95, upper 95% CI.
8
rs2410558
22
7
rs10249476
rs13815
3
rs582537
19
5
rs299295
ERCC6
DCLRE1B
CINP
CHEK1
CASP3
ABCB4
Gene
0.1818
0.2273
0.2139
0.36
0.4545
0.3376
0.46
0.3182
0.4581
0.28
0.5
0.3032
0.36
0.4545
0.3679
0.04
0.2727
0.1263
0.2083
0.4091
0.3115
0.44
0.5
0.3995
0.32
0.2727
0.1979
0.1
0.04545
0.09896
0.22
0.09091
0.1536
0.22
0.25
0.2266
0.06
0
0.02865
0.42
0.4545
0.2891
0.18
0.4
0.1432
MAF
controls
A
A
A
T
T
T
G
G
G
C
C
C
G
G
G
C
C
C
G
G
G
C
C
C
C
C
C
G
G
G
C
C
C
C
C
C
C
C
C
T
T
T
G
G
G
Major
allele
.903
.031
.006
.862
.125
.029
.728
.919
.017
.834
.071
.024
.470
.125
.007
.481
.072
.022
.360
.356
.024
.372
.999
.007
.929
.435
.024
.512
.204
.028
.823
.247
.019
.051
.358
.032
.046
.109
.031
.029
.251
.009
.112
.879
.042
P
0.9
NA
0.5001
0.8889
0.3429
0.6539
0.7826
1.071
0.651
0.8571
0.2857
0.6295
0.5926
0.3429
0.5961
NA
0.1569
0.481
1.9
1.806
1.504
1.782
1
1.596
1.063
1.697
1.576
1.8
4.2
1.755
1.182
2.857
1.667
3.545
1.909
1.52
5.222
NA
2.409
0.1255
0.4615
0.5769
NA
0.9
0.5557
OR
0.1643
NA
0.3031
0.2346
0.08519
0.4459
0.1965
0.2838
0.4575
0.202
0.07114
0.4208
0.142
0.08519
0.4078
NA
0.01696
0.2544
0.4743
0.5123
1.054
0.4973
0.2877
1.132
0.2784
0.4472
1.059
0.3044
0.3973
1.059
0.2723
0.4585
1.083
0.9522
0.4772
1.034
0.9057
NA
1.059
0.01503
0.1221
0.3804
NA
0.2331
0.3133
L95
4.929
NA
0.8253
3.367
1.38
0.9592
3.117
4.046
0.9263
3.636
1.148
0.9416
2.473
1.38
0.8716
NA
1.451
0.9094
7.611
6.363
2.147
6.385
3.476
2.251
4.055
6.439
2.346
10.64
44.4
2.909
5.13
17.8
2.565
13.2
7.638
2.233
30.11
NA
5.481
1.049
1.745
0.875
NA
3.475
0.9857
U95
.999
.037
.007
.999
.328
.033
.800
.999
.017
.999
.226
.024
.857
.126
.010
.999
.113
.017
.517
.450
.026
.591
.999
.005
.999
.999
.041
.560
.261
.035
.999
.632
.023
.096
.357
.036
.060
.146
.044
.087
.345
.006
.149
.857
.041
Empirical
P
Trial
E1A100
Hovon-50
Myeloma IX
E1A100
Hovon-50
Myeloma IX
E1A100
Hovon-50
Myeloma IX
E1A100
Hovon-50
Myeloma IX
E1A100
Hovon-50
Myeloma IX
E1A100
Hovon-50
Myeloma IX
E1A100
Hovon-50
Myeloma IX
E1A100
Hovon-50
Myeloma IX
E1A100
Hovon-50
Myeloma IX
E1A100
Hovon-50
Myeloma IX
E1A100
Hovon-50
Myeloma IX
E1A100
Hovon-50
Myeloma IX
E1A100
Hovon-50
Myeloma IX
E1A100
Hovon-50
Myeloma IX
E1A100
Hovon-50
Myeloma IX
rs1002153, rs2048426, rs2267669
rs1002153, rs2048426, rs2267669
rs1002153, rs2048426, rs2267669
rs2227314
rs2227314
rs2227314
In Linkage
JOHNSON et al
rs20579
10
14
rs7011
rs4253211
11
rs506504
1
4
rs1049216
rs12022378
7
rs2302387
Chromosome
4928
SNP
Table 1. Allele distributions of cross-trial–validated associated thalidomide-related VTE SNPs
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BLOOD, 15 DECEMBER 2008 䡠 VOLUME 112, NUMBER 13
From www.bloodjournal.org by guest on June 16, 2017. For personal use only.
rs207932
rs207932
4929
were also removed from the analysis. As a result of this process we
found 24 SNPs (Table 1 ) associated with VTE in the “discovery
set” with consistent distributions in the 2 validation datasets.
Haplotype analysis showed that 6 SNPs were in linkage with a
stronger proxy SNP and as such were discarded, leaving
18 validated SNPs associated with thalidomide-related VTEs.
A “forest plot” with odds ratios (ORs) and confidence intervals
(CIs) for the combined and individual datasets of the validated
SNPs was generated (Figure 3).
E1A100
To examine gene-gene interactions, we looked for pairwise combinations mediating risk. The epistatic interactions with a P value less
than .001 are shown in Table S10.
.226
Recursive partioning analysis
.189
2.403
0.6351
9.088
To maximize the size of the dataset and thus to maximize the ability
to identify relevant SNPs, we combined all the datasets into one
and randomly split it into a two-thirds training set and one-third
validation set. The data were stratified by trial and VTE patients to
ensure that the training and validation sets were comparable. These
data included 165 subjects without VTE and 84 subjects with VTE
in the training set and 82 subjects without VTE and 42 subjects
with VTE in the validation set. The training set was used to identify
the top associated genes and SNPs by association at the level of
P less than .05 listed in Table S2. These SNPs were used in a
recursive partitioning analysis carried out on the test set with the
aim of finding the combination with the best sensitivity and
specificity for the identification of VTE. We pruned the tree to find
the tree with the highest classification and smallest number of
SNPs. The results of this analysis (Figure 4) showed that using
7 SNPs (rs7011 in CINP, rs289747 in CETP, rs610529 in
ALDH1A1, rs3829963 in CDKN1A, rs2608555 in GAN, rs699947
in VEGF, and andrs168351 in ALDH1A) it was possible to identify
VTEs correctly in 70% of individuals with a specificity of 59% and
sensitivity of 81%. This set of SNPs performed well in the
validation set; the set was able to correctly classify VTEs in 61% of
individuals with a specificity of 30% and sensitivity of 77% (Tables
S12 and S13).
Discussion
0.2292
NA indicates not applicable; L95, lower 95% CI; and U95, upper 95% CI.
T
0.4167
C
rs207932
Myeloma IX
Hovon-50
.857
.047
1.998
7.186
0.3131
0.9828
1.401
1.5
.611
.062
0.1818
0.25
C
0.3486
0.4286
T
T
Untranslated
XRCC5
2
rs2440
G
0.36
C
rs11862958
rs11862958
E1A100
.800
1.804
0.07006
0.3556
.198
rs11862958
0.1667
A
Hovon-50
.380
2.26
0.1473
0.5769
.428
0.4
G
0.2778
16
rs12922317
TNFRSF17
Intron,Tag-SNP
G
0.3958
A
E1A100
Myeloma IX
.010
0.9004
0.4317
0.6234
.011
Hovon-50
0.29
A
.999
.999
3.241
3.539
0.2749
0.2362
0.875
0.9864
.983
.842
0.42
T
0.3636
0.4167
C
C
0.3333
T
Myeloma IX
.029
0.9728
0.4827
0.6852
.034
0.4562
7
rs2070682
SERPINE1
Intron
C
0.365
T
P
MAF
controls
Major
allele
SNPS ASSOCIATED WITH THALIDOMIDE VTES IN MYELOMA
Gene-gene interactions
MAF
patients
Minor
allele
Functional class
Gene
Chromosome
SNP
Table 1. Allele distributions of cross-trial–validated associated thalidomide-related VTE SNPs (continued)
OR
L95
U95
Empirical
P
Trial
In Linkage
BLOOD, 15 DECEMBER 2008 䡠 VOLUME 112, NUMBER 13
This study has analyzed data from 3 large randomized clinical
studies comprising 3100 patients, comparing induction treatment
for newly presenting patients with myeloma with and without
thalidomide. The results of this analysis show that the background
rate of VTE in MP-treated patients is very low and significantly
increases with the addition of thalidomide. In addition we provide
further evidence that infusional regimes based on VAD increase
VTE rates to around 15%, which is similar to the rates seen with
oral thalidomide combinations. The nature of the thrombotic events
is qualitatively different between regimes, with all events being
either DVT or pulmonary embolism (PE) in the oral thalidomidetreated patients, whereas in the intravenous treatment, 50% of the
events are central line related. There is a doubling of non–central
line–related VTE rates in the thalidomide-treated patients compared with those receiving infusional induction regimens. The
median time to VTE in each of the treatments is approximately
50 to 60 days after the initiation of treatment, a time reflecting the
rapid dissolution of the myeloma clone. We have shown previously
that response rate is enhanced in thalidomide-containing regimes
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4930
JOHNSON et al
BLOOD, 15 DECEMBER 2008 䡠 VOLUME 112, NUMBER 13
Figure 3. Forest plots showing distribution of validated SNPs associated with thalidomide-related VTEs across the Myeloma IX, Hovon-50, and ECOG EA100 trials.
Error bars indicate upper and lower 95% CIs.
compared with VAD-like regimes, and we postulate that this is
important in determining the VTE risk.64 The mechanistic importance of increased response rates with VTE risk may explain the
reduced numbers of VTEs seen in relapsed patients, who are
frequently drug resistant and show lower response rates. It is also
important not to discount increased VTE risk due to changes in the
disease biology related procoagulant profiles of such relapsed
patients.
Using a nested case-control design with readily defined exposure and clinical endpoint, this study has given useful information
about inherited genetic variants with a moderate effect size
affecting the thrombotic response to thalidomide exposure. We
chose to use the MRC Myeloma IX study as our initial discovery
set because it was the largest and had the most data available with
it. Validation in the combined Hovon-50/ECOG study represents a
pragmatic decision based on study size, study design, and our
desire to identify penetrant variants that can be replicated with
relevance to different studies and datasets.
Despite a comprehensive analysis of the genetic variation
within the coagulation and prothrombotic pathways, we could
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BLOOD, 15 DECEMBER 2008 䡠 VOLUME 112, NUMBER 13
SNPS ASSOCIATED WITH THALIDOMIDE VTES IN MYELOMA
4931
Figure 4. Predictive tree of thalidomide-related thrombosis in myeloma patients following recursive partitioning analysis.
not find evidence for a significant association of genetic
variation within these pathways with VTE risk following
thalidomide exposure. Although we found Factor 5 Leiden
(rs6025) to be associated with an increased risk of VTE in this
analysis, the thrombotic risk was not increased in patients
treated with thalidomide; similar results were seen for polymorphisms in MTHFR and FGB. We saw no association with
thalidomide-related VTE in commonly reported VTE risk alleles
in F2-455G/A (rs3136430) splice variant 20210G/A (rs3136431).
We did find weak associations with genes known to mediate the
coagulation pathway, including MTRR, PLAUR, PPARD,
PPARGC1A, PPARGC1B, THBS4, and WNK, but the associated
risk was not high. We conclude that we can exclude a major
contribution of genetic variation within the coagulation and
prothrombotic pathways based on this targeted approach, although smaller contributions to the phenotype may be missed
because of the study size and design. Our findings are consistent
with previous clinical observations and work by some of the
authors, who failed to identify relevant changes in functional
assays investigating this pathway.65-67
The lack of a strong association with variation in the coagulation cascade suggests that VTE risk is mediated via alternative
mechanisms. We identified the 18 SNPs, which validated across the
3 datasets (Figure 3). Using the whole BOAC panel as the
background gene set in the DAVID Functional Annotation Clustering tool against the 18 validated genes, generated 3 major enriched
annotation clusters. The annotation clusters consisted of 2 “response to stress” groups; a response to DNA damage group,
including CHEK1, XRCC5, LIG1, ERCC6, DCLRE1B, and PARP1;
a cytokine response group containing NFKB1, TNFRSF17, IL12B,
and LEP; and a third related group of “apoptosis” with CASP3,
PPARD, and NFKB1. These enrichment groups indicate that
genetic variation in response to DNA damage and cytokinemediated apoptosis modulates risk of developing a thalidomiderelated thrombosis.
High-dose dexamethasone enhances hemostasis, increases
platelet activation, and promotes von Willebrand factor (VWF)
antigen–dependent thrombosis.68 Extremely high levels of factor VIII coagulant (FVIII:C) activity and VWF have been found
in thalidomide-exposed patients.69 Patients that develop a
subsequent VTE had higher VWF antigen (Ag) levels but not
FVIII:C levels. High FVIII:C/VWF Ag levels are found in
patients with active myeloma; this is probably a reflection of
increased bone marrow angiogenesis in myeloma. These prothrombogenic circumstances would contribute to VTE during
treatment with a thalidomide-dexamethasone combination.69 In
line with VWF mediating the prothrombotic effects of dexamethasone in thalidomide-related VTE, we saw a protective effect of
VWF nonsynonymous SNP (rs216321) and synonymous SNP
(rs216902) in thalidomide-treated controls.
Although there is evidence to suggest thalidomide may
damage DNA directly,70 it is important to note that most patients
in this analysis were derived from the MRC and Hovon-50
studies, which included either cyclophosphamide or doxorubicin/
adriamycin in the treatment regime, which may explain an
association with DNA repair genes. Variation in DNA repair
capacity could readily affect the response of the myeloma clone
to treatment due to the direct relationship between the extent of
DNA damage accumulation and the clinical response to alkylating agents.71 A rapid response and dissolution of myeloma
clones with an impaired double-stranded DNA repair pathway
would release greater prothrombotic factors that could be either
microparticles with surface tissue factor or cytokines and tissue
factor. The greater thrombogenesis due to increased dissolution
of the myeloma clone may act additively with a dexamethasonethalidomide interaction on plasma cells,72 giving rise to an
increased number of VTEs in the MRC and Hovon-50 studies.73,74 An alternative mechanism to explain the increased risk
of a VTE associated with DNA repair genes could be based on
the observation that thalidomide can protect endothelial cells
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4932
JOHNSON et al
BLOOD, 15 DECEMBER 2008 䡠 VOLUME 112, NUMBER 13
Figure 5. Thalidomide treatment in combination with alkylating agents in myeloma promotes prothrombotic conditions at the endothelium surface via a
combination of mechanisms. Mechanisms include rapid apoptosis of myeloma cells leading to circulating tissue factor (TF), exposed TF by endothelium cells salvaged from
apoptosis, increased circulating cytokines (eg, TNF␣) with T-cell activation by antigen-presenting cells (APCs), and activated platelets in response to increased circulating
cytokines.
from doxorubicin-induced apoptosis by restoring PAR-1 expresssion,75 promoting subendothelial tissue factor exposure, endothelial dysfunction, and platelet activation, and consequently
increasing the thrombosis risk.75-77 Under these conditions,
decreased DNA repair capacity could promote clot formation at
the endothelium (Figure 5).
The enrichment of cytokine-mediated apoptosis genes in SNPs
associated with thalidomide-related thrombosis risk may also give
clues to the role bortezomib and aspirin play in VTE management.
Low rates of VTE are seen in patients with myeloma treated with
bortezomib in thalidomide combinations,37,40,78 possibly through
the prevention of the up-regulation of prothrombotic molecules
such as thrombomodulin, cytokines, and E-selectin by bortezomib.79,80 A number of clinical studies have suggested that
aspirin42,81-89 is effective at preventing the excess of VTEs seen in
thalidomide-exposed individuals. Aspirin is classically thought to
inhibit platelet COX-2, reducing platelet adhesiveness and modulating risk of arterial thrombosis. Aspirin can also lead to decreased
levels of circulating TNF-␣ by inhibiting IKK and therefore NF␬B.
Higher levels of TNF-␣ and COX-2 lead to an increased risk of
apoptosis in endothelial cells, which also become proadhesive to
nonactivated platelets.90 In a thalidomide treatment setting, aspirin
may be able to inhibit thalidomide VTE-mediated events by
lowering circulating TNF-␣.
Genetic analysis of the multifactorial phenotype that is
thalidomide related venous thrombosis is challenging. To minimize experimental artifacts that can be found in many associa-
tion studies,91 we have associated a discrete clinical outcome
from a homogenous population of similarly treated patients with
high-quality genotype data with stringent quality controls. We
took a hypothesis-driven candidate gene approach rather than a
whole genome scan (WGS)–based approach because it was clear
that the number of events to be analyzed would be small, and we
were aiming to identify pertinent functional loci variants with
moderate to large effect size. We accept that future GWS and
sequencing approaches may add relevant variants in unknown
pathways. As part of the analysis, we took an exploratory
approach to defining whether the SNPs identified could be used
to identify patients at high risk of VTE and consequently guide
clinical intervention. Guidelines have recently been established
to govern clinical indicators for intervention, but these prognostic factors can be difficult to identify and use clinically.41 The
US Food and Drug Administration (FDA) and European Medicines Evaluation Agency (EMEA) have published warnings
suggesting the use of thromboprophylaxis with any immunomodulatory derivative of thalidomide (IMiD)–based regimen.92-94 The results of this recursive partitioning analysis have
identified a limited number of SNPs that, when analyzed
together, can predict the risk of VTE. Testing for these SNPs has
the potential for being clinically useful for identifying high-risk
patients for whom therapeutic intervention is required. For
clinically defined high-risk patients intervention strategies may
not change, but for patients at genetic high risk for whom aspirin
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BLOOD, 15 DECEMBER 2008 䡠 VOLUME 112, NUMBER 13
was the chosen strategy, intervention with warfarin or LMWH
would be more appropriate.
Acknowledgments
We would like to thank the staff at the Haematological Malignancy
Diagnostic Service, Leeds, and members of the MRC Myeloma IX
Trial Management Group, as well as consultants and patients
entered in the MRC Myeloma IX, ECOG, and Hovon-50 trials.
This study was supported by the International Myeloma Foundation (IMF) as part of the Bank on a Cure (BOAC) Consortium.
We are also grateful for support received from Myeloma UK.
SNPS ASSOCIATED WITH THALIDOMIDE VTES IN MYELOMA
4933
Authorship
Contribution: C.J. wrote the paper, performed research, and
analyzed data; S.C. and C.R. performed research; A.H.,
K.H. N.J.D., J.H., J.A.C., S.E.B., G.H.J., and J.C. analyzed data;
H.G., D.B., V.R., P.S., and B.V.N. designed the research; F.E.D.
and G.J.M. designed the research and wrote the paper; and B.D.
designed the research and analyzed data.
Conflict-of-interest disclosure: The authors declare no competing financial interests.
Correspondence: G. J. Morgan, Section of Haemato-Oncology,
Institute of Cancer Research, Belmont, Sutton, Surrey, United
Kingdom SM2 5NG; e-mail: [email protected].
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From www.bloodjournal.org by guest on June 16, 2017. For personal use only.
2008 112: 4924-4934
doi:10.1182/blood-2008-02-140434 originally published
online September 19, 2008
Genetic associations with thalidomide mediated venous thrombotic
events in myeloma identified using targeted genotyping
David C. Johnson, Sophie Corthals, Christine Ramos, Antje Hoering, Kim Cocks, Nicholas J.
Dickens, Jeff Haessler, Harmut Goldschmidt, J. Anthony Child, Sue E. Bell, Graham Jackson, Dalsu
Baris, S. Vincent Rajkumar, Faith E. Davies, Brian G. M. Durie, John Crowley, Pieter Sonneveld,
Brian Van Ness and Gareth J. Morgan
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